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States versus Rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning
Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outco...
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| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2010
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2895323/ https://ncbi.nlm.nih.gov/pubmed/20510862 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuron.2010.04.016 |
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